Week 5: Data-Driven Decision-Making Skills

Enhancing Business Decision-Making through understanding data analysis.

This week’s article delves into the critical intersection of decision-making processes in business and the integration of data analysis to enhance the quality and effectiveness of a leader’s decisions. By examining a decision-making framework and exploring the pivotal role of data analysis within this context, this article aims to provide leaders with insights into leveraging data to inform knowledgeable and intuitive decision-making. It also explores issues related to data quality, the need for advanced analytics skills, and the ethical implications of data usage. This week debuts the partnership with Folk Media Studio to develop our interview series on “Leaders and Supporters.” For this topic, we interview Lieutenant Colonel (LTC) Melissa Sayers, a professional data scientist for the U.S. Army, to discuss how she incorporates data analysis to support her leaders' decision-making process.

LTC Melissa Sayers is currently serving as an Operations Research Systems Analyst (ORSA) in the United States Army at First Army, Rock Island Arsenal, Illinois. Melissa has nine years of experience using data to solve business problems and communicate with operational leaders. She builds data engineering pipelines, teaches data literacy, and leverages optimization and prediction to enable decisions. Her recent experience focuses on transportation tracking, efficient business operations, financial auditability, and recruiting challenge identification. She routinely codes in Python (Spark, pandas), R (tidyverse, RStudio), and SQL using tools like Foundry, DataBricks, Amazon Web Services ec2 instances, PowerBI, and Tableau. Melissa holds a Master of Computer Science in Data Science from the University of Illinois at Urbana/Champaign. She also holds a Bachelors in Physics and a Bachelors in Mechanical Engineering. She is a member of the Military Operations Research Society where she is a co-chair and co-founder of the Analytic Capability Development working group focused on manning, training, and equipping data-focused professionals.

LTC Sayers discusses her techniques for supporting her leaders with decision-making. Below is a sample of what we discussed. To see the full version and more on this topic, become a paid subscriber.

The views expressed by the speakers in this interview are those of the speakers and do not necessarily reflect the official policy or position of the Department of the Army, DoD, or U.S. Government.

The sources for this article:

1. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.

2. LaValle, S., Hopkins, M. S., Lesser, E., Shockley, R., & Kruschwitz, N. (2010). Analytics: The new path to value. MIT Sloan Management Review, 52(1), 1–8.

3. Wu, J., & Du, S. (2017). A conceptual framework for understanding the application of big data in the hotel industry. International Journal of Contemporary Hospitality Management, 29(12), 3085–3103.

4. Week 5: “Data-Driven Decision-Making Skills.” Leaders and Supporters Series, Folk Media Studio, 31 January 2024, https://thedavarkgroup.substack.com/.

In the dynamic landscape of business, effective decision-making is a cornerstone of organizational success. First, we want to discuss a good decision-making process that not only incorporates successful methodologies but also places a significant emphasis on the integration of data analysis. By grounding decisions in data-driven insights, leaders can navigate uncertainties with greater precision, agility, and speed.

Decision-Making Framework

The foundation of a sound decision-making process lies in a well-defined framework. I will draw upon established business literature and management theories, as well as my experiences with hasty and deliberate decisions. Below, I will discuss key stages, data inputs, milestones, implementation planning, and outputs that provide a structured approach for leaders to follow.

Stage 1: Identify the Mission or Problem.

Decision-making requires an understanding of a problem and a solution, or an opportunity sets a mission that is necessary to execute. Leaders should understand those two actions and identify them early. This requires the leader and supporters to see themselves in their current state. The team will then start to develop a future direction the organization must achieve. To plan, you must know the 5 W’s: “who,” “what,” “when,” “where,” and “why.” The organization also needs to understand the assets available and the time required to plan. Time is a major factor in whether the leader can execute a deliberate or intuitive decision.

Your organization will have a more efficient time developing the future state when they have systems or processes in place to see a problem before it is detrimental. It also helps leaders to truly understand the mission, see our week 2 article on back briefs and confirmation briefs. This means that you must set up and process the correct data tied to this step. Data such as assets available for the mission or problem, available work hours, budget, limitations to what is possible, and planning assumptions. This stage’s output is a briefing to all relevant members on the problem or mission and assumptions that will inform what you need to change into facts to continue planning.

Stage 2: Gather Data, Materials, and Team

Now that you know the direction you need to travel and collect the information that I highlighted above, it is important to set up the collaboration system and bring in the team. A collaboration system will assist in developing the decision, publishing it, distributing tasks to execute the plan, and evaluating the decision after completion. Are you conducting this over distances, or are all resources local? What systems do you have in place to publish the decision, who else needs to know? Other important data points to gather are how you will evaluate this decision and what mechanisms are needed to implement changes.

The right team is a very important step. People execute actions, using tools, and they are critical to implementing decisions. It is easy to overlook personnel that you might think do not apply to this decision-making process, but in my experience, some of the best inputs come from team members you wouldn’t expect. People, such as leaders, are also needed to provide input to the decision-making process so the team can frame their work in the right direction. Having the wrong people will lead to bad decisions or improper execution of the decision. If done correctly, the team you build will greatly increase the quality and productivity of the information you output in this stage.

Stage 3: Analyze the Course of Action

The team should brainstorm as many ways to solve the problem or accomplish the mission as they can. As you complete this step, then leaders can apply those limitations or screening criteria that make a course of action impractical. The team should then evaluate each course of action, based on developed criteria, that assist with identifying the best to the worst course of action. Leader input is important to understand these evaluation criteria and what is important.

Data will assist in this stage greatly. Using the right research for your data, you can tailor your data mining and modeling to quickly eliminate variables not necessary to the decision. Modeling can assist with making a time-consuming process take days, minutes, or seconds. Data analysis also makes your evaluations concrete versus relying on overly subjective criteria. A decision supported with data becomes much more sound, deliberate, and validated. Using data correctly will allow more deliberate decisions for an organization that once had to rely on hasty decision-making. Even though hasty decision-making relies on intuition and remains a powerful tool, it has a higher probability of failure, especially with bad information. You want the output of this stage to be a defined decision briefing that allows leaders to make a proper, well-thought-out decision.

Stage 4: Develop the Plan of Action

Throughout each of these stages, assumptions that receive validation through data become facts. The team can now use these facts, data, and courses of action to develop a proper plan that solves the problem or completes the mission. Planners in the team are also able to develop the management or people who will execute this plan of action. Once you know all of these key points, leaders can apply resources, timelines, and tasks to accomplish the plan. Also, leaders can input their managing milestones to judge completion and evaluate success.

Stage 5: Execute and Evaluate

The last stage is to manage tasks for the team, achieve milestones, and judge whether the problem was solved or the mission was complete. Organizations should publish their documents on their collaboration systems to ensure all participants know operations. Leaders can use those collaboration systems to manage the process to completion. The organization should also have a process to evaluate success and develop ways to adapt in the future.

Data analysis can assist in this process immensely. There is now computer automation to track and input milestone completion, evaluate task efficiency, and much more. Organizations can establish data centers that collect the right variables and use data analysis tools to immediately create dashboards or reports that help leaders manage the execution of plans. Data can also use the same analysis to evaluate whether an organization can do those same tasks again. Leaders can quickly interpret which actions or decisions achieved the desired outcomes or even how to adjust an action for better outcomes. The team can use this information to provide an after-action review, or postmortem, to make better decisions in the future.

Data analysis is a pivotal component in each stage of the decision-making process. Data analysis enables leaders to assess the current state of affairs, identify trends, and anticipate potential outcomes. By harnessing both historical and real-time data, decision-makers can make informed choices that align with organizational goals and respond effectively to changing market dynamics. It can do this with significant speed, eliminating rash or bad decisions.

Utilizing Data for Strategic Insights

The section emphasizes the role of data in strategic decision-making. Through case studies and practical examples, it illustrates how organizations can leverage data to gain strategic insights. This includes utilizing predictive analytics to forecast market trends, assessing customer behavior through data mining, and optimizing operational efficiency by analyzing internal processes. Such strategic insights empower leaders to make decisions that not only address immediate challenges but also contribute to long-term sustainability and growth.

Understanding your market is a sign of business intelligence. We have moved away from business-to-customer marketing to having direct conversations and feedback with customers (Chen, H., Chiang, R. H., & Storey, V. C., 2012). Businesses can now use data analytics and business intelligence to analyze market changes, trends, and shifts within seconds of it happening. An organization can even stay ahead of changes in the market with predictive analysis. A data model such as association rules can help businesses establish links between market or customer interactions with products that they produce or should produce. You have seen this used effectively in shopping businesses when they recommend items that are associated with your purchases based on the multitude of others who have bought similar products.

Applying these analytical tools allows leaders to quickly react to a problem, and as we discussed above, develop decisions to steer the organization in the right direction. The research paper authored by Wu, J., & Du, S. (2017) cites several studies that illustrated how big data and business intelligence were unequivocal to growth and providing value to their customers. Also, “Top-performing companies are three times more likely than lower performers to be sophisticated users of analytics, and are two times more likely to say that their analytics use is a competitive differentiator” (LaValle, S., Hopkins, M. S., Lesser, E., Shockley, R., & Kruschwitz, N., 2010).

Challenges and Considerations

While advocating for data-driven decision-making, I want to address challenges and considerations. Acknowledging these challenges provides a holistic understanding of the nuances associated with integrating data analysis into decision-making processes. Like any tool, the team will rely on data quality, the need for advanced analytics skills, and the proper use of ethical implications of data usage to succeed. This article continues with actions you can use to reduce these challenges with the data pyramid below.